Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities

Shaygan, Maryam; Meese, Collin; Li, Wanxin; Zhao, Xiaoliang; Nejad, Mark

doi:10.1016/j.trc.2022.103921

Cited by 77 publications

(47 citation statements)

References 293 publications

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“…ML allows significant improvements in our daily lives. Among other things, it provides much better traffic prediction, and it also makes automatic translations more accurate (Shaygan et al 2022 ; Agrawal et al 2019 , 41). Of course, ML algorithms also show inherent technical limitations.…”

Section: The Use Of ML In Bail Decisionsmentioning

confidence: 99%

Machine learning in bail decisions and judges’ trustworthiness

Morin-Martel

2023

AI & Soc

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The use of AI algorithms in criminal trials has been the subject of very lively ethical and legal debates recently. While there are concerns over the lack of accuracy and the harmful biases that certain algorithms display, new algorithms seem more promising and might lead to more accurate legal decisions. Algorithms seem especially relevant for bail decisions, because such decisions involve statistical data to which human reasoners struggle to give adequate weight. While getting the right legal outcome is a strong desideratum of criminal trials, advocates of the relational theory of procedural justice give us good reason to think that fairness and perceived fairness of legal procedures have a value that is independent from the outcome. According to this literature, one key aspect of fairness is trustworthiness. In this paper, I argue that using certain algorithms to assist bail decisions could increase three different aspects of judges’ trustworthiness: (1) actual trustworthiness, (2) rich trustworthiness, and (3) perceived trustworthiness.

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Section: The Use Of ML In Bail Decisionsmentioning

confidence: 99%

Machine learning in bail decisions and judges’ trustworthiness

Morin-Martel

2023

AI & Soc

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“…Thus, researchers present the data sets utilized in the literature and made available for use, and authors compare the quantitative results of precision of the various techniques, highlighting advantages and limitations, allowing someone to identify the related challenges and, from there, suggest an overall taxonomy in which the knowledge gained in this traffic flow evaluation merges from a computational perspective. A certain investigation is to give a complete overview of traffic prediction approaches in order to lay the groundwork for comprehending the open research issues in traffic prediction [8]. Because of its rapid progress and promise in traffic prediction, through a focus on multivariate traffic time series forecasting, they zoom in on the latest developments and new research prospects in AI-based traffic forecasting approaches.…”

Section: Related Workmentioning

confidence: 99%

Urban Traffic Flow Estimation System Based on Gated Recurrent Unit Deep Learning Methodology for Internet of Vehicles

et al. 2023

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Congestion in the world's traffic systems is a major issue that has far-reaching repercussions, including wasted time and money due to longer commutes and more frequent stops for gas. Modern scholarly challenges arise alongside chances to greatly enhance traffic prediction made possible by the integration of modern technologies into transportation systems. Various techniques have been utilized for the purpose of traffic flow prediction, including statistical, machine learning, and deep neural networks. In this paper, deep neural network architecture based on long short term memory (LSTM), bi-directional version, and gated recurrent units (GRUs) layers have been structured to build the deep neural network, in order to predict the performance of the traffic flow in four distinct junctions which has a great impact on the Internet of vehicles' applications. The structure comprised of sixteen-layers, five of them are GRU-layers and one bi-directional LSTM-layer. The dataset employed in this work involved four congested junctions. The dataset extended from the first of November 2016 to 30th of June 2017. Cleaning and preprocessing operations were achieved on the dataset before feeding it to the designed deep neural network of this paper. Results show that the suggested method produced a comparable performance with respect to state-of-the art approaches.

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“…(5) after collecting the necessary endorsements, package them into an endorsed transaction and send it to the regional BL blockchain orderers; (6) wait to receive the updated regional parameters from the DHFA group; (7) decrypt the updated regional parameters; and lastly, update their local version of the regional prediction model G e .…”

Section: Online Federated Learning Of Traffic Predictionmentioning

confidence: 99%

“…This scenario is not practical given the increasing decentralization of ITS, where participants are heterogeneous with respect to data volumes and computing resources and collect location-specific data sets. The reader is referred to our recent critical review on traffic prediction methods [6] for more details.…”

Section: Introductionmentioning

confidence: 99%

B²SFL: A Bi-Level Blockchained Architecture for Secure Federated Learning-Based Traffic Prediction

Guo,

Meese,

et al. 2023

IEEE Trans. Serv. Comput.

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Federated Learning (FL) is a privacy-preserving machine learning (ML) technology that enables collaborative training and learning of a global ML model based on aggregating distributed local model updates. However, security and privacy guarantees could be compromised due to malicious participants and the centralized FL server. This paper proposed a bi-level blockchained architecture for secure federated learning-based traffic prediction. The bottom and top layer blockchain store the local model and global aggregated parameters accordingly, and the distributed homomorphic-encrypted federated averaging (DHFA) scheme addresses the secure computation problems. We propose the partial private key distribution protocol and a partially homomorphic encryption/decryption scheme to achieve the distributed privacy-preserving federated averaging model. We conduct extensive experiments to measure the running time of DHFA operations, quantify the read and write performance of the blockchain network, and elucidate the impacts of varying regional group sizes and model complexities on the resulting prediction accuracy for the online traffic flow prediction task. The results indicate that the proposed system can facilitate secure and decentralized federated learning for real-world traffic prediction tasks.

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Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities

Cited by 77 publications

References 293 publications

Machine learning in bail decisions and judges’ trustworthiness

Machine learning in bail decisions and judges’ trustworthiness

Urban Traffic Flow Estimation System Based on Gated Recurrent Unit Deep Learning Methodology for Internet of Vehicles

B²SFL: A Bi-Level Blockchained Architecture for Secure Federated Learning-Based Traffic Prediction

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Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities

Cited by 77 publications

References 293 publications

Machine learning in bail decisions and judges’ trustworthiness

Machine learning in bail decisions and judges’ trustworthiness

Urban Traffic Flow Estimation System Based on Gated Recurrent Unit Deep Learning Methodology for Internet of Vehicles

B2SFL: A Bi-Level Blockchained Architecture for Secure Federated Learning-Based Traffic Prediction

Contact Info

Product

Resources

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B²SFL: A Bi-Level Blockchained Architecture for Secure Federated Learning-Based Traffic Prediction